As the amount of data requiring storage increases from sources such as online applications, the need for a more efficient processing system also increases. Recently, the use of traditional data extraction, transformation, and loading (ETL) tools has become impractical, time consuming and costly as data is received in increasingly higher volumes. Traditionally, ETL tools have been used in data warehousing projects, or other projects such as data storage in a database, or the like, when the data will later be accessed and analyzed. These existing ETL tools generally require manual intervention and/or are not able to process large volumes of data in parallel, both leading to processing inefficiencies.